Past Pandemics

Research question

  • Are patterns of pandemic spread, its determinants, and effects of public health interventions are similar across pandemics?
  • Estimate excess mortality for pandemics in 1890, 1918 and 2020 per district, age groups and sex
  • Comparing of spatial pattern between the pandemics
  • Investigate the determinants of spread in the context of different co-factors ( Urbanization, GIP per capita etc.)

Data

  • Collected and digitalized from Kaspar Staub’s team
  • Russian flu: 1879 - 1895
  • Spanish fl: 1908 - 1925
  • Covid19 : 2020

Population

  • Modern data population for all districts and all age groups and sex available
  • Census 1888 and 1910 population data for all districts and age groups and sex, but 1900 and 1920 are not collect -> might a problem
  • Census 1880, 1888, 1900, 1910, 1920 census data for all districts

    Estimation:
  • Interpolation for total and each districts (census 1880, 1888, 1900, 1910, 1920)
  • Calculation of age distribution for 1888 and 1910
  • Take age distribution of 1888 to interpolate population between 1880 to 1900 and age distribution from 1910 to interpolate population between 1900 and 1920
  • Maybe a student from Kaspar will also collect detailed census data from 1900 and 1920, would be a bit more precise then

Maps

  • All districts are harmonised so that 1890, 1918 and 2020 have the same districts.
  • Many districts from 1879 - 1920 have been combined so that they are the same as 2014-2020.
  • Schauffhausen is only one district, as death data is only available for Schaffhausen as a whole.
  • In Solothurn, the districts are merged as in 1876 - 1920.
  • New shapefiles created via QGIS to have one map for all years (to make them comparable).

Methods

  • Prior: Default Gamma distribution
  • Estimation of the expected death counts for each district, sex and age
  • Poisson Regression
  • Natural logarithm of the population in each district (sex, age) was used as the model’s offset term
  • Estimation based on the mortality trend of the previous 4 years (1890 only 4 years possible)
  • Pandemic years 1890, 1918 and 2020 are excluded to estimate expected mortality (high mortalities in these years, what would be observed without a pandemic?)
  • Bootstrapping to address the uncertainty in observed death counts and to provide a prediction interval (PI) for the predicted mortality(resampled N = 1000)
  • Excess mortality = observed death counts – expected death counts
  • Excess mortality is shown relatively in percentage (Excess mortality/expected death counts)
  • To compare the districts of each pandemic year the relative excess mortality was normalized
  • To find pattern and clusters: Moran’s I statistics ( LISA: Local Indicators of Spatial Association) were used for mapping statistically significant local clusters

Preliminary Results

Total

Relative yearly numbers of excess deaths

Maps

LISA (Local Indicators of Spatial Association)

Sex

Maps

LISA (Local Indicators of Spatial Association)

Age

  • Only two age groups 0-69 and >=70.
  • More precise age groups would lead to a lot of zeros in small districts
  • Even with two age groups, zeros in some districts

Maps

LISA (Local Indicators of Spatial Association)

Next steps, Points to be discussed

  1. Bayesian approaches in hierarchical modelling (INLA)
  • Lot of zeros, especially for age groups
  • Bayesian approaches in hierarchical modelling (INLA) to investigate the spatial pattern of excess mortality per district
  1. Groups
  • Too many age groups lead to too many zeros -> too many zeros are also a problem with INLA
  • Maybe only 0-69 and >70 or 0-40, 41-69, >70
  1. Further Co-factors (I have to discuss with Kaspar):
  • Urbanization
  • Infant mortality rates as a proxy for health index
  • Public health intervention for each district (canton)
  • GDP per capita as proxy for SES
  • Population density (population/km2)
  • Proportion of children, 5–15 y (as school-age children are thought to drive influenza transmission)